import time
import warnings
from abc import ABC
from copy import deepcopy
from typing import Optional

import torch

from ..utils import add_start_docstrings, logging


A						=			logging.get_logger(__name__)


A						=			r'\n    Args:\n        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):\n            Indices of input sequence tokens in the vocabulary.\n\n            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and\n            [`PreTrainedTokenizer.__call__`] for details.\n\n            [What are input IDs?](../glossary#input-ids)\n        scores (`torch.FloatTensor` of shape `(batch_size, config.vocab_size)`):\n            Prediction scores of a language modeling head. These can be scores for each vocabulary token before SoftMax\n            or scores for each vocabulary token after SoftMax.\n        kwargs (`Dict[str, Any]`, *optional*):\n            Additional stopping criteria specific kwargs.\n\n    Return:\n        `bool`. `False` indicates we should continue, `True` indicates we should stop.\n\n'





class     lowerCAmelCase__     (				SCREAMING_SNAKE_CASE_			):


		'''simple docstring'''






		@add_start_docstrings(snake_case__		)
		def __call__(       self     :     Optional[int]     ,  snake_case__     :     torch.LongTensor     ,  snake_case__     :     torch.FloatTensor     ,  **snake_case__     :     Any		)      ->	bool:
				raise NotImplementedError('StoppingCriteria needs to be subclassed'		)





class     lowerCAmelCase__     (				SCREAMING_SNAKE_CASE_			):


		'''simple docstring'''






		def __init__(       self     :     Union[str, Any]     ,  snake_case__     :     int     ,  snake_case__     :     Optional[int] = None		)      ->	List[Any]:
				_lowerCamelCase				   =  max_length
				_lowerCamelCase				   =  max_position_embeddings



		@add_start_docstrings(snake_case__		)
		def __call__(       self     :     str     ,  snake_case__     :     torch.LongTensor     ,  snake_case__     :     torch.FloatTensor     ,  **snake_case__     :     str		)      ->	bool:
				_lowerCamelCase				   =  input_ids.shape[-1]
				_lowerCamelCase				   =  cur_len >= self.max_length
				if self.max_position_embeddings is not None and not is_done and cur_len >= self.max_position_embeddings:
						logger.warning_once(
						    'This is a friendly reminder - the current text generation call will exceed the model\'s predefined '
						    f"""maximum length ({self.max_position_embeddings}). Depending on the model, you may observe """
						    'exceptions, performance degradation, or nothing at all.'		)
				return is_done





class     lowerCAmelCase__     (				SCREAMING_SNAKE_CASE_			):


		'''simple docstring'''






		def __init__(       self     :     Optional[int]     ,  snake_case__     :     int     ,  snake_case__     :     int		)      ->	Optional[int]:
				warnings.warn(
				    'The class `MaxNewTokensCriteria` is deprecated. '
				    f"""Please use `MaxLengthCriteria(max_length={start_length + max_new_tokens})` """
				    'with `max_length = start_length + max_new_tokens` instead.'     ,  snake_case__     ,  )
				_lowerCamelCase				   =  start_length
				_lowerCamelCase				   =  max_new_tokens
				_lowerCamelCase				   =  start_length + max_new_tokens



		@add_start_docstrings(snake_case__		)
		def __call__(       self     :     int     ,  snake_case__     :     torch.LongTensor     ,  snake_case__     :     torch.FloatTensor     ,  **snake_case__     :     Optional[int]		)      ->	bool:
				return input_ids.shape[-1] >= self.max_length





class     lowerCAmelCase__     (				SCREAMING_SNAKE_CASE_			):


		'''simple docstring'''






		def __init__(       self     :     List[str]     ,  snake_case__     :     float     ,  snake_case__     :     Optional[float] = None		)      ->	List[str]:
				_lowerCamelCase				   =  max_time
				_lowerCamelCase				   =  time.time() if initial_timestamp is None else initial_timestamp



		@add_start_docstrings(snake_case__		)
		def __call__(       self     :     Dict     ,  snake_case__     :     torch.LongTensor     ,  snake_case__     :     torch.FloatTensor     ,  **snake_case__     :     List[str]		)      ->	bool:
				return time.time() - self.initial_timestamp > self.max_time





class     lowerCAmelCase__     (				SCREAMING_SNAKE_CASE_			):


		'''simple docstring'''






		@add_start_docstrings(snake_case__		)
		def __call__(       self     :     List[Any]     ,  snake_case__     :     torch.LongTensor     ,  snake_case__     :     torch.FloatTensor     ,  **snake_case__     :     Tuple		)      ->	bool:
				return any(criteria(snake_case__     ,  snake_case__		) for criteria in self		)



		@property
		def   _snake_case       (       self     :     int		)      ->	Optional[int]:
				for stopping_criterium in self:
						if isinstance(snake_case__     ,  snake_case__		):
								return stopping_criterium.max_length
						elif isinstance(snake_case__     ,  snake_case__		):
								return stopping_criterium.max_length
				return None



def 	lowerCamelCase					(	UpperCamelCase     :				StoppingCriteriaList			,       UpperCamelCase     :				int							)   ->    StoppingCriteriaList:
		_lowerCamelCase				   =  stopping_criteria.max_length
		_lowerCamelCase				   =  deepcopy(UpperCamelCase							)
		if stopping_max_length is not None and stopping_max_length != max_length:
				warnings.warn('You set different `max_length` for stopping criteria and `max_length` parameter'			,       UpperCamelCase							)
		elif stopping_max_length is None:
				new_stopping_criteria.append(MaxLengthCriteria(max_length=UpperCamelCase							)							)
		return new_stopping_criteria
def 	lowerCamelCase					(	UpperCamelCase     :				int			,       UpperCamelCase     :				int							)   ->    str:
		if number < 0 or shift_amount < 0:
				raise ValueError('both inputs must be positive integers'							)

		_lowerCamelCase				   =  str(bin(UpperCamelCase							)							)
		binary_number += "0" * shift_amount
		return binary_number


def 	lowerCamelCase					(	UpperCamelCase     :				int			,       UpperCamelCase     :				int							)   ->    str:
		if number < 0 or shift_amount < 0:
				raise ValueError('both inputs must be positive integers'							)

		_lowerCamelCase				   =  str(bin(UpperCamelCase							)							)[2:]
		if shift_amount >= len(UpperCamelCase							):
				return "0b0"
		_lowerCamelCase				   =  binary_number[: len(UpperCamelCase							) - shift_amount]
		return "0b" + shifted_binary_number


def 	lowerCamelCase					(	UpperCamelCase     :				int			,       UpperCamelCase     :				int							)   ->    str:
		if number >= 0:  # Get binary representation of positive number
				_lowerCamelCase				   =  '0' + str(bin(UpperCamelCase							)							).strip('-'							)[2:]
		else:  # Get binary (2's complement) representation of negative number
				_lowerCamelCase				   =  len(bin(UpperCamelCase							)[3:]							)  # Find 2's complement of number
				_lowerCamelCase				   =  bin(abs(UpperCamelCase							) - (1 << binary_number_length)							)[3:]
				_lowerCamelCase				   =  (
				    '1' + '0' * (binary_number_length - len(UpperCamelCase							)) + binary_number
				)

		if shift_amount >= len(UpperCamelCase							):
				return "0b" + binary_number[0] * len(UpperCamelCase							)
		return (
		    "0b"
		    + binary_number[0] * shift_amount
		    + binary_number[: len(UpperCamelCase							) - shift_amount]
		)


if __name__ == "__main__":
				import doctest

				doctest.testmod()